{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/iterative-global-similarity-points-a-robust","title":"Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration","arxiv_id":"1808.03899","date":"2018-08-12","proceeding":null,"authors":["Yue Pan","Bisheng Yang","Fuxun Liang","Zhen Dong"],"abstract":"In this paper, we propose a coarse-to-fine integration solution inspired by\nthe classical ICP algorithm, to pairwise 3D point cloud registration with two\nimprovements of hybrid metric spaces (eg, BSC feature and Euclidean geometry\nspaces) and globally optimal correspondences matching. First, we detect the\nkeypoints of point clouds and use the Binary Shape Context (BSC) descriptor to\nencode their local features. Then, we formulate the correspondence matching\ntask as an energy function, which models the global similarity of keypoints on\nthe hybrid spaces of BSC feature and Euclidean geometry. Next, we estimate the\nglobally optimal correspondences through optimizing the energy function by the\nKuhn-Munkres algorithm and then calculate the transformation based on the\ncorrespondences. Finally,we iteratively refine the transformation between two\npoint clouds by conducting optimal correspondences matching and transformation\ncalculation in a mutually reinforcing manner, to achieve the coarse-to-fine\nregistration under an unified framework.The proposed method is evaluated and\ncompared to several state-of-the-art methods on selected challenging datasets\nwith repetitive, symmetric and incomplete structures.Comprehensive experiments\ndemonstrate that the proposed IGSP algorithm obtains good performance and\noutperforms the state-of-the-art methods in terms of both rotation and\ntranslation errors.","url_abs":"http://arxiv.org/abs/1808.03899v1","url_pdf":"http://arxiv.org/pdf/1808.03899v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"iterative-global-similarity-points-a-robust","repo_url":"https://github.com/YuePanEdward/GH-ICP","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"point-cloud-registration","task_name":"Point Cloud Registration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.03899","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}